Aphid-YOLO: A Lightweight Detection Model for Real-Time Identification and Counting of Aphids in Complex Field Environments

Abstract

Aphids are among the most destructive pests that threaten global crop yields, harming crops through feeding and virus transmission. Accurate detection of aphids in fields is a crucial step in implementing sustainable agricultural pest management. However, the tiny size of aphids and the complex image background present significant challenges for accurate identification and classification for in-field detection. In response to the challenges, this study proposes a lightweight real-time object detection model, Aphid-YOLO (A-YOLO), for in-field aphid identification and counting. Specifically, a tiny path aggregation network with C2f-CG modules is proposed to enhance the detection ability of tiny objects while maintaining a low computational cost through efficiently fusing multilayer features. For model training, a normalized Wasserstein distance loss function is adopted to address the optimization challenges caused by the tiny size of aphids. In addition, an optimized data augmentation method, Mosaic9, is introduced to enrich training samples and positive supervised signals for addressing the classification challenge of tiny aphids. To validate the effectiveness of A-YOLO, this study conducts comprehensive experiments on an aphid detection dataset with images collected by hand-held devices from a complex field environment. Experimental results demonstrate that A-YOLO achieves outstanding detection efficiency, with an mAP@0.5 of 83.4%, an mAP@0.5:0.95 of 33.7%, an inference speed of 72 FPS, and a model size of 30.6 MB. Compared to the YOLOv8m model employing traditional Mosaic data augmentation, the proposed method improves mAP@0.5 by 5.8%, mAP@0.5:0.95 by 2.7%, increases inference speed by 5 FPS, and reduces model size by 38.4% .